A neuro-fuzzy technique for fault diagnosis and its application to rotating machinery

Reliability Engineering and System Safety

Volumn 94, Issue 1, 2009, Pages 78-88

  Zio, Enrico ^a   Gola, Giulio ^a  

^a POLITECNICO DI MILANO   (Italy)

Author keywords

Fault classification; Fuzzy logic; Neural networks; Rotating machinery

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONDITION MONITORING; ELECTRIC FAULT CURRENTS; HEALTH; MAINTENANCE;

APPLICATIONS.; BEARING FAULTS; CLASSIFICATION MODELS; DIAGNOSTIC PROBLEM; FAULT CLASSIFICATION; FAULT DIAGNOSIS; FUZZY LOGIC; HIGH RATES; INCIPIENT FAULTS; MACHINE CONDITION MONITORING; MAINTENANCE COSTS; NEURAL NETWORKS; NEURO-FUZZY; NEURO-FUZZY APPROACHES; PATTERN CLASSIFICATIONS; ROTATING MACHINERY;

INDUSTRIAL APPLICATIONS;

EID: 52149119162     PISSN: 09518320     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ress.2007.03.040     Document Type: Article

References (34)

1. Ragulskis K., and Yurkauskas A. Vibration of bearings (1989), Hemisphere, Bristol, PA
2. Lipovszky G., Solyomvari K., and Varga G. Vibration testing of machines and their maintenance (1990), Elsevier, Amsterdam, The Netherlands
3. House J.M., Lee W.Y., and Shin D.R. Classification techniques for fault detection and diagnosis of an air-handling unit. ASHRAE Trans 105 1 (1999) 1987-1997
4. Robinson J.C., Canada R.G., and Piety K.R. PeakVue analysis-new methodology for bearing fault detection. Sound Vib 30 11 (1996) 22-25
5. Korablev S., Shapin V., and Filatov Y. Vibration diagnostics in precision instruments (1989), Hemisphere, Bristol, PA
6. Agarwal J., Blockley D., and Woodman N. Vulnerability of structural systems. Struct Saf J 25 3 (2003)
7. Donley M., Stokes W., Jeong G.S., Suh K.K., and Jung S.G. Validation of finite element models for noise/vibration/harshness simulation. Sound Vib. 30 8 (1996) 18-23
8. Chow M.-Y., and Yee S.-O. An adaptive back-propagation through time training algorithm for a neural controller. Proc IEEE Int Symp Intell Control 1 (1991) 170-175
9. Chow M.-Y., Lee Y.-S., and Trussell H.J. Motor Incipient Fault Detection using Artificial Neural Network and Fuzzy Logic Technologies, Computer Aided Maintenance, Methodology and Practices (1996), Chapman & Hall, London, UK
10. Li B., Chow M.-Y., Tipsuwan Y., and Hung J.C. Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans Ind Electr 47 5 (2000) 1-2
11. Muller B., and Reinhardt J. Neural networks-an introduction (1990), Springer, New York
12. Rumelhart D.E., and McClelland J.L. Parallel distributed processing, 1 (1986), MIT Press, Cambridge, MA
13. Zadeh L.A. Fuzzy sets. Inform Control 8 (1965) 338-353
14. Ross T.J. Fuzzy logic with engineering applications (2004), Wiley, London, UK
15. Marseguerra M., Zio E., and Baraldi P. A fuzzy modelling approach to the identification of transients in nuclear components. Ann. Nucl. Energy 31 (2004) 2093-2112
16. Chen S, Xu Y. A kind of least squares support vector machines for pattern classification. Proceedings of the 6th international FLINS conference; 2004. p. 308-13.
17. Habbi A, Zelmat M. Design of a fuzzy model-based controller for a drum boiler-turbine system. Proceedings of the 6th international FLINS conference; 2004. p. 649-54.
18. Du J, Er MJ. Fault diagnosis in air-handling unit system using dynamic fuzzy neural network. Proceedings of the 6th international FLINS conference, 2004. p. 483-8.
19. Marseguerra M., Zio E., and Avogadri P. Model identification by neuro-fuzzy techniques: predicting the water level in a steam generator of a PWR. Prog Nucl Energy 44 3 (2004) 237-252
20. Czogaa E, Czogala E, Leski J. Fuzzy and neuro-fuzzy intelligent systems. 2000.
21. Harris C, Gan Q, Hong X, Adaptive modelling, estimation and fusion from data: a neuro-fuzzy approach; 2002.
22. Lin C.T., and Lee C.S.G. Neural-network-based fuzzy logic control and decision system. IEEE Trans Comput 40 12 (1991) 1320-1336
23. Jang J.S. ANFIS: Adaptive network-based fuzzy inference systems. IEEE Trans Syst Man Cybern 23 3 (1993) 665-685
24. Juang C.F., Nein H.W., and Lin C.T. Techniques and applications of integrated neural-network based fuzzy logic control systems. Fuzzy Theory Syst Tech Appl 3 (1999) 1265-1295
25. Mitra S., and Hayashi Y. Neuro-fuzzy rule generation: survey in soft computing framework. IEEE Trans Neural Netw (2000)
26. Nauck D., and Kruse R. A neuro-fuzzy method to learn fuzzy classification rules from data. Fuzzy Sets Systems 89 (1997) 277-288
27. De Oliveira J.V. Semantic constraints for membership function optimization. IEEE Trans Syst Man Cybernet Part A: Syst Hum 29 1 (1999) 128-138
28. Castellano G., Fanelli A.M., and Mencar C. A fuzzy clustering approach for mining diagnostic rules. IEEE conference on systems, man, cybernetics (2003)
29. Goddu G., Li B., Chow M.Y., and Hung J.C. Motor bearing fault diagnosis by a fundamental frequency amplitude based fuzzy decision system. IEEE Trans Syst Man Cybernet (1998) 1961-1965
30. Wang L.X., and Mendel J.M. Fuzzy basis functions, universal approximation and orthogonal least-squares learning. IEEE Trans Neural Netw 3 5 (1992) 807-814
31. Yang M. A survey of fuzzy clustering. Math Comput Model 18 11 (1993) 1-16
32. Zio E., and Baraldi P. Evolutionary fuzzy clustering for the classification of transients in nuclear components. Prog Nucl Energy 46 3-4 (2005)
33. Lin C.T., and Lee C.S.G. Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems. IEEE Trans Fuzzy Systems 2 1 (1994) 46-63
34. Kilundu B, Dehombreux P. A feature selection approach based on principal component analysis and synaptic weights for mechanical fault diagnosis. In: Kolowrocki, editor. Proceedings of ESREL 2005, Advances in safety and reliability. London: Taylor & Francis; 2005. p. 1041-6.